1 Introduction

We collected saliva samples before and after entering the scanner. Those samples were then poured together for OT analysis.

## [1] "Number of subjects with OT Data at T1: "
## 
## FND  HC 
##  59  65
## [1] " "
## [1] "Mean Age at T1: "
## # A tibble: 2 × 5
##   group variable     n  mean    sd
##   <fct> <fct>    <dbl> <dbl> <dbl>
## 1 FND   age         59  38.4  14.1
## 2 HC    age         65  33.2  11.2
## [1] "Significantly different: "
## [1] 0.0256064

1.1 Oxytocin FND vs. HC

We first visualize the data.

## [1] FALSE


1.1.1 Statistical Analysis OT FND vs. HC

I applied an ANOVA on the fitted data (using lm), so that we correct/include covariates, which are:

  • Gender
  • Psychotropic Medication
  • Bdi
  • STAI-1
  • Menstrual Cycle
  • Contraception
  • Menopause
  • Age
  • ##                Df Sum Sq Mean Sq F value Pr(>F)
    ## group           1   18.4   18.37   1.330  0.251
    ## gender          1   14.5   14.48   1.048  0.308
    ## psychMed        1    1.8    1.83   0.132  0.717
    ## bdi             1    0.1    0.12   0.009  0.926
    ## stai1           1    0.0    0.02   0.002  0.966
    ## date_diff       1   34.0   34.02   2.463  0.119
    ## contraception   2   18.7    9.37   0.679  0.509
    ## age             1    8.8    8.80   0.637  0.427
    ## Residuals     114 1574.5   13.81


    There is no significant group difference in FND vs. HC. Also, covariates do not seem to affect the results strongly.

    2 Genetic association study

    Previously, we saw that rs53576 was significantly associated to the diagnosis of FND (= risk factor). We now first replicate this in the subset of our sample.

    We start with quality control.

    1. Hardy Weinberg Equilibrium
    ##           alleles major.allele.freq HWE      missing (%)
    ## rs1360780 C/T     65.7              0.230253 0          
    ## rs1491850 T/C     63.3              0.698607 0          
    ## rs1799732 G/I     89.5              0.358274 0          
    ## rs1800532 G/T     57.7              0.042245 0          
    ## rs2254298 G/A     86.3              1.000000 0          
    ## rs3758653 T/C     81.0              1.000000 0          
    ## rs3800373 A/C     70.2              0.131083 0          
    ## rs4570625 G/T     77.8              0.794373 0          
    ## rs53576   G/A     60.9              0.850544 0          
    ## rs6265    C/T     83.1              0.750615 0

    All fine, except of rs1800532.

    Now we run the association study. These are our genes:
  • TPH2 rs4570625
  • TPH1 rs1800532
  • BDNF rs6265
  • BDNF rs1491850
  • DRD4 rs3758653
  • OXTR rs2254298
  • OXTR rs53576
  • DRD2 rs1799732
  • FKBP51 rs1360780
  • FKBP52 rs3800373
  • TPH1 rs1800532
  • Covariates are:
  • Age
  • Gender
  • CTQ total score
  • BDI
  • STAI-2
  • We first give an overview over all p-values.

    ##           comments codominant dominant recessive log-additive
    ## rs1360780        -    0.97963  0.97297   0.84020      0.90533
    ## rs1491850        -    0.61674  0.99334   0.36466      0.64470
    ## rs1799732        -    0.80801        -         -            -
    ## rs1800532        -    0.54478  0.46094   0.28771      0.29488
    ## rs2254298        -    0.23474  0.11969   0.66313      0.17044
    ## rs3758653        -    0.85104  0.84949   0.57143      0.72493
    ## rs3800373        -    0.71642  0.90302   0.41608      0.66630
    ## rs4570625        -    0.80468  0.51253   0.91672      0.55731
    ## rs53576          -    0.04556  0.69019   0.02845      0.37433
    ## rs6265           -    0.50057  0.26657   0.98025      0.36224
    ## $rs1360780
    ## 
    ## SNP: rs1360780  adjusted by: age gender ctq bdi stai2 
    ##               0    %  1    %   OR lower upper p-value   AIC
    ## Codominant                                                 
    ## C/C          26 40.0 24 40.7 1.00              0.9796 140.7
    ## C/T          34 52.3 29 49.2 1.01  0.40  2.57              
    ## T/T           5  7.7  6 10.2 0.86  0.18  4.18              
    ## Dominant                                                   
    ## C/C          26 40.0 24 40.7 1.00              0.9730 138.8
    ## C/T-T/T      39 60.0 35 59.3 0.98  0.40  2.41              
    ## Recessive                                                  
    ## C/C-C/T      60 92.3 53 89.8 1.00              0.8402 138.7
    ## T/T           5  7.7  6 10.2 0.86  0.19  3.83              
    ## log-Additive                                               
    ## 0,1,2        65 52.4 59 47.6 0.96  0.48  1.91  0.9053 138.7
    ## 
    ## $rs1491850
    ## 
    ## SNP: rs1491850  adjusted by: age gender ctq bdi stai2 
    ##               0    %  1    %   OR lower upper p-value   AIC
    ## Codominant                                                 
    ## T/T          26 40.0 25 42.4 1.00              0.6167 139.8
    ## C/T          28 43.1 27 45.8 1.21  0.45  3.21              
    ## C/C          11 16.9  7 11.9 0.63  0.17  2.32              
    ## Dominant                                                   
    ## T/T          26 40.0 25 42.4 1.00              0.9933 138.8
    ## C/T-C/C      39 60.0 34 57.6 1.00  0.41  2.47              
    ## Recessive                                                  
    ## T/T-C/T      54 83.1 52 88.1 1.00              0.3647 137.9
    ## C/C          11 16.9  7 11.9 0.57  0.17  1.93              
    ## log-Additive                                               
    ## 0,1,2        65 52.4 59 47.6 0.87  0.47  1.60  0.6447 138.5
    ## 
    ## $rs1799732
    ## 
    ## SNP: rs1799732  adjusted by: age gender ctq bdi stai2 
    ##               0    %  1    %   OR lower upper p-value   AIC
    ## Codominant                                                 
    ## G/G          51 78.5 47 79.7 1.00               0.808 138.7
    ## G/I          14 21.5 12 20.3 0.87   0.3  2.58              
    ## log-Additive                                               
    ## 0,1,2        65 52.4 59 47.6 0.87   0.3  2.58         138.7
    ## 
    ## $rs1800532
    ## 
    ## SNP: rs1800532  adjusted by: age gender ctq bdi stai2 
    ##               0    %  1    %   OR lower upper p-value   AIC
    ## Codominant                                                 
    ## G/G          28 43.1 19 32.2 1.00              0.5448 139.5
    ## G/T          25 38.5 24 40.7 1.16  0.42  3.25              
    ## T/T          12 18.5 16 27.1 1.87  0.60  5.84              
    ## Dominant                                                   
    ## G/G          28 43.1 19 32.2 1.00              0.4609 138.2
    ## G/T-T/T      37 56.9 40 67.8 1.41  0.57  3.51              
    ## Recessive                                                  
    ## G/G-G/T      53 81.5 43 72.9 1.00              0.2877 137.6
    ## T/T          12 18.5 16 27.1 1.73  0.63  4.79              
    ## log-Additive                                               
    ## 0,1,2        65 52.4 59 47.6 1.35  0.77  2.38  0.2949 137.7
    ## 
    ## $rs2254298
    ## 
    ## SNP: rs2254298  adjusted by: age gender ctq bdi stai2 
    ##               0    %  1    %   OR lower upper p-value   AIC
    ## Codominant                                                 
    ## G/G          49 75.4 43 72.9 1.00              0.2347 137.9
    ## G/A          15 23.1 15 25.4 2.41  0.83  6.95              
    ## A/A           1  1.5  1  1.7 0.40  0.00 89.54              
    ## Dominant                                                   
    ## G/G          49 75.4 43 72.9 1.00              0.1197 136.3
    ## G/A-A/A      16 24.6 16 27.1 2.27  0.80  6.45              
    ## Recessive                                                  
    ## G/G-G/A      64 98.5 58 98.3 1.00              0.6631 138.6
    ## A/A           1  1.5  1  1.7 0.33  0.00 58.97              
    ## log-Additive                                               
    ## 0,1,2        65 52.4 59 47.6 1.96  0.75  5.14  0.1704 136.9
    ## 
    ## $rs3758653
    ## 
    ## SNP: rs3758653  adjusted by: age gender ctq bdi stai2 
    ##               0    %  1    %   OR lower upper p-value   AIC
    ## Codominant                                                 
    ## T/T          42 64.6 39 66.1 1.00              0.8510 140.4
    ## T/C          20 30.8 19 32.2 0.98  0.37  2.60              
    ## C/C           3  4.6  1  1.7 0.48  0.04  6.54              
    ## Dominant                                                   
    ## T/T          42 64.6 39 66.1 1.00              0.8495 138.7
    ## T/C-C/C      23 35.4 20 33.9 0.91  0.35  2.36              
    ## Recessive                                                  
    ## T/T-T/C      62 95.4 58 98.3 1.00              0.5714 138.4
    ## C/C           3  4.6  1  1.7 0.49  0.04  6.44              
    ## log-Additive                                               
    ## 0,1,2        65 52.4 59 47.6 0.86  0.38  1.96  0.7249 138.6
    ## 
    ## $rs3800373
    ## 
    ## SNP: rs3800373  adjusted by: age gender ctq bdi stai2 
    ##               0    %  1    %   OR lower upper p-value   AIC
    ## Codominant                                                 
    ## A/A          30 46.2 27 45.8 1.00              0.7164 140.1
    ## C/A          33 50.8 27 45.8 0.97  0.38  2.43              
    ## C/C           2  3.1  5  8.5 2.10  0.30 14.71              
    ## Dominant                                                   
    ## A/A          30 46.2 27 45.8 1.00              0.9030 138.7
    ## C/A-C/C      35 53.8 32 54.2 1.06  0.43  2.59              
    ## Recessive                                                  
    ## A/A-C/A      63 96.9 54 91.5 1.00              0.4161 138.1
    ## C/C           2  3.1  5  8.5 2.14  0.32 14.08              
    ## log-Additive                                               
    ## 0,1,2        65 52.4 59 47.6 1.17  0.56  2.45  0.6663 138.6
    ## 
    ## $rs4570625
    ## 
    ## SNP: rs4570625  adjusted by: age gender ctq bdi stai2 
    ##               0    %  1    %   OR lower upper p-value   AIC
    ## Codominant                                                 
    ## G/G          39 60.0 35 59.3 1.00              0.8047 140.3
    ## G/T          23 35.4 22 37.3 1.38  0.53  3.60              
    ## T/T           3  4.6  2  3.4 1.26  0.14 11.14              
    ## Dominant                                                   
    ## G/G          39 60.0 35 59.3 1.00              0.5125 138.3
    ## G/T-T/T      26 40.0 24 40.7 1.36  0.54  3.44              
    ## Recessive                                                  
    ## G/G-G/T      62 95.4 57 96.6 1.00              0.9167 138.7
    ## T/T           3  4.6  2  3.4 1.12  0.13  9.37              
    ## log-Additive                                               
    ## 0,1,2        65 52.4 59 47.6 1.26  0.58  2.74  0.5573 138.4
    ## 
    ## $rs53576
    ## 
    ## SNP: rs53576  adjusted by: age gender ctq bdi stai2 
    ##               0    %  1    %   OR lower upper p-value   AIC
    ## Codominant                                                 
    ## G/G          23 35.4 22 37.3 1.00             0.04556 134.6
    ## G/A          36 55.4 25 42.4 0.54  0.20  1.52              
    ## A/A           6  9.2 12 20.3 2.82  0.71 11.13              
    ## Dominant                                                   
    ## G/G          23 35.4 22 37.3 1.00             0.69019 138.6
    ## G/A-A/A      42 64.6 37 62.7 0.83  0.33  2.10              
    ## Recessive                                                  
    ## G/G-G/A      59 90.8 47 79.7 1.00             0.02845 134.0
    ## A/A           6  9.2 12 20.3 3.96  1.13 13.93              
    ## log-Additive                                               
    ## 0,1,2        65 52.4 59 47.6 1.34  0.70  2.59 0.37433 138.0
    ## 
    ## $rs6265
    ## 
    ## SNP: rs6265  adjusted by: age gender ctq bdi stai2 
    ##               0    %  1    %   OR lower upper p-value   AIC
    ## Codominant                                                 
    ## C/C          42 64.6 44 74.6 1.00              0.5006 139.4
    ## C/T          21 32.3 13 22.0 0.55  0.20  1.51              
    ## T/T           2  3.1  2  3.4 0.86  0.10  7.73              
    ## Dominant                                                   
    ## C/C          42 64.6 44 74.6 1.00              0.2666 137.5
    ## C/T-T/T      23 35.4 15 25.4 0.58  0.22  1.53              
    ## Recessive                                                  
    ## C/C-C/T      63 96.9 57 96.6 1.00              0.9803 138.8
    ## T/T           2  3.1  2  3.4 1.03  0.12  9.04              
    ## log-Additive                                               
    ## 0,1,2        65 52.4 59 47.6 0.69  0.31  1.53  0.3622 137.9
    ## 
    ## attr(,"label.SNPs")
    ##  [1] "rs1360780" "rs1491850" "rs1799732" "rs1800532" "rs2254298" "rs3758653"
    ##  [7] "rs3800373" "rs4570625" "rs53576"   "rs6265"   
    ## attr(,"models")
    ## [1] 1 2 3 5
    ## attr(,"quantitative")
    ## [1] FALSE

    Only rs53576 looks interesting. There might be some signifcances in others, but the allele distribution across the groups is not significant (e.g., when it would be 3 vs. 50).

    2.1 Genetic association between genotype and Oxytocin

    I run a linear regression analysis with Oxytocin (OT) as dependent variable to see if there is association between genotype and OT. Adding again the covariates on menstrual cycle, menopause and contraception.

    ##           comments codominant dominant recessive
    ## rs1360780        -    0.24443  0.40117   0.10405
    ## rs1491850        -    0.44059  0.34579   0.24867
    ## rs1799732        -    0.85294        -         -
    ## rs1800532        -    0.63537  0.44318   0.41093
    ## rs2254298        -    0.53858  0.33000   0.46675
    ## rs3758653        -    0.05724  0.38617   0.01770
    ## rs3800373        -    0.67376  0.40296   0.62775
    ## rs4570625        -    0.23332  0.82846   0.08998
    ## rs53576          -    0.15723  0.08712   0.16703
    ## rs6265           -    0.89436  0.68671   0.89928
    ## 
    ## SNP: rs3758653  adjusted by: age date_diff menopause contraception ctq bdi stai2 
    ##                n     me     se      dif   lower upper p-value   AIC
    ## Codominant                                                         
    ## T/T           81  7.250 0.3567 0.000000               0.05966 685.2
    ## T/C           39  7.603 0.6323 0.240025 -1.1795 1.660              
    ## C/C            4 12.250 3.6291 4.743394  0.8767 8.610              
    ## Dominant                                                           
    ## T/T           81  7.250 0.3567 0.000000               0.39409 688.6
    ## T/C-C/C       43  8.035 0.6775 0.613494 -0.7921 2.019              
    ## Recessive                                                          
    ## T/T-T/C      120  7.365 0.3154 0.000000               0.01845 683.3
    ## C/C            4 12.250 3.6291 4.656574  0.8391 8.474              
    ## Overdominant                                                       
    ## T/T-C/C       85  7.486 0.3886 0.000000               0.99045 689.3
    ## T/C           39  7.603 0.6323 0.008793 -1.4273 1.445              
    ## log-Additive                                                       
    ## 0,1,2                          0.940362 -0.2863 2.167 0.13572 686.9

    We see a significant association with rs3758653. However, when we have a closer look, we see that the results are driven by 4 subjects that are carring the C/C variation. So there results are not biologically significant.

    Next we can also do it group-specific:

    ## [1] "Association between OT and genotypes in FND: "
    ##           comments codominant dominant recessive
    ## rs1360780        -    0.51791  0.33047   0.37956
    ## rs1491850        -    0.13232  0.05720   0.18140
    ## rs1799732        -    0.92119        -         -
    ## rs1800532        -    0.99337  0.90880   0.95598
    ## rs2254298        -    0.76579  0.46282   0.88792
    ## rs3758653        -    0.98417  0.99755   0.85957
    ## rs3800373        -    0.54587  0.28900   0.53858
    ## rs4570625        -    0.78293  0.48940   0.73336
    ## rs53576          -    0.18370  0.07917   0.21619
    ## rs6265           -    0.79367  0.64857   0.74784
    ## [1] "Association between OT and genotypes in HC: "
    ##           comments codominant dominant recessive
    ## rs1360780        -    0.78614  0.90786   0.51992
    ## rs1491850        -    0.83658  0.67501   0.81036
    ## rs1799732        -    0.18844        -         -
    ## rs1800532        -    0.19029  0.11724   0.14484
    ## rs2254298        -    0.70211  0.41728   0.66367
    ## rs3758653        -    0.02698  0.40992   0.00696
    ## rs3800373        -    0.93278  0.96034   0.70786
    ## rs4570625        -    0.17051  0.90256   0.06969
    ## rs53576          -    0.67383  0.37331   0.78290
    ## rs6265           -    0.44022  0.27674   0.68752

    There is nothing significant when we split into the two groups (except of rs3758653, which we found to be only 4 subjects). This means that there is no association between genotype and salivary OT. Caveat Later we do an analysis where we split into the three genotypes for rs53576, where we see results.

    3 Methylation Data on OXTR

    We analysed 5 amplicons of the OXTR receptor. We take the mean of them, which gives us an average methylation rate of the OXTR promoter region. This is standard and has also been done like this in Apazoglou et al. 

    We again first visualize the data. We first replicate Apazoglou et al., who just run a simple student’s t-test on the mean methylation data. Then we statistically compare them between groups using again the ANOVA on the fitted data.

    Covariates used:
  • Gender
  • Psychotropic Medication
  • Bdi
  • STAI-1
  • Menstrual Cycle
  • Contraception
  • Menopause
  • Age

  • ## [1] FALSE

    ## [1] "Replication Apazoglou et al.:"
    ## 
    ##  Welch Two Sample t-test
    ## 
    ## data:  dfOT$OXTR_CpG_sum[dfOT$group == "FND"] and dfOT$OXTR_CpG_sum[dfOT$group == "HC"]
    ## t = 1.2951, df = 114.87, p-value = 0.1979
    ## alternative hypothesis: true difference in means is not equal to 0
    ## 95 percent confidence interval:
    ##  -0.004820328  0.023029140
    ## sample estimates:
    ## mean of x mean of y 
    ## 0.3977155 0.3886111
    ## [1] "ANOVA on fitted data using Covariates.:"
    ##                     Df Sum Sq Mean Sq F value  Pr(>F)   
    ## OXTR_CpG_sum         1    7.1    7.15   0.546 0.46141   
    ## group                1   10.1   10.11   0.772 0.38147   
    ## psychMed             1    5.8    5.83   0.445 0.50604   
    ## bdi                  1    3.0    3.03   0.231 0.63162   
    ## stai1                1    1.2    1.18   0.090 0.76456   
    ## date_diff            1   13.2   13.17   1.006 0.31807   
    ## contraception        2   29.7   14.83   1.133 0.32587   
    ## menopause            1    3.9    3.89   0.297 0.58668   
    ## age                  1    4.4    4.40   0.336 0.56319   
    ## OXTR_CpG_sum:group   1  116.7  116.75   8.921 0.00348 **
    ## Residuals          109 1426.5   13.09                   
    ## ---
    ## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    ## 3 observations deleted due to missingness

    FINDINGS: We have a significant OXTR Methylation x Group effect on salivary oxytocin. We want to decouple this effect by looking at the groups individually. We see that the effects of OXTR Methylation goes in the different direction in FND versus HC.

    3.1 Interaction between OT, OXTR Methylation and Genotype

    As a next step, we investigate some more interaction models. We have the following potential oxytocin related data:

  • Group
  • rs53576
  • OXTR Methylation
  • Salivary Oxytocin

  • We can make combinations of interactions with all of them. Mostly we find significant results. We just have to think what actually makes sense. Here some of the results:

    ## [1] "OXTR Methylation ~ Group*genotype with Oxytocin as covariate"
    ##                Df  Sum Sq  Mean Sq F value   Pr(>F)    
    ## group           1 0.00250 0.002503   2.245  0.13700    
    ## rs53576         2 0.02284 0.011422  10.245 8.56e-05 ***
    ## Oxytocin        1 0.00001 0.000009   0.008  0.92793    
    ## psychMed        1 0.00270 0.002701   2.423  0.12254    
    ## bdi             1 0.00284 0.002844   2.551  0.11323    
    ## stai1           1 0.00027 0.000267   0.239  0.62560    
    ## date_diff       1 0.00004 0.000043   0.039  0.84419    
    ## contraception   2 0.01201 0.006003   5.384  0.00593 ** 
    ## menopause       1 0.00897 0.008967   8.043  0.00547 ** 
    ## age             1 0.00087 0.000870   0.780  0.37901    
    ## group:rs53576   2 0.00729 0.003643   3.267  0.04197 *  
    ## Residuals     106 0.11818 0.001115                     
    ## ---
    ## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    ## 3 observations deleted due to missingness
    ## [1] FALSE


    Particularly, the Genotype seems to have an effect on OXTR Methylation in FND. Let’s compare it statistically.
    AA carriers have higher OXTR Methylation in comparison to GA or GG. While there are no differences between GA or GG. This effect is not found in Oxytocin, nor in healthy controls.

    ## [1] "OXTR Methylation ~ genotype in FND only"
    ##               Df  Sum Sq  Mean Sq F value   Pr(>F)    
    ## rs53576        2 0.02837 0.014183  16.310 4.17e-06 ***
    ## psychMed       1 0.00399 0.003994   4.593   0.0373 *  
    ## bdi            1 0.00337 0.003371   3.877   0.0549 .  
    ## stai1          1 0.00110 0.001104   1.269   0.2656    
    ## date_diff      1 0.00006 0.000064   0.074   0.7867    
    ## contraception  2 0.01153 0.005767   6.632   0.0029 ** 
    ## menopause      1 0.00301 0.003008   3.459   0.0692 .  
    ## age            1 0.00142 0.001417   1.629   0.2081    
    ## Residuals     47 0.04087 0.000870                     
    ## ---
    ## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    ## 1 observation deleted due to missingness
    ##   Tukey multiple comparisons of means
    ##     95% family-wise confidence level
    ## 
    ## Fit: aov(formula = lm(formula = OXTR_CpG_sum ~ rs53576 + psychMed + bdi + stai1 + date_diff + contraception + menopause + age, data = dfOT_FND))
    ## 
    ## $rs53576
    ##               diff         lwr         upr     p adj
    ## GA-AA -0.049208333 -0.07427130 -0.02414536 0.0000568
    ## GG-AA -0.058779762 -0.08460528 -0.03295425 0.0000044
    ## GG-GA -0.009571429 -0.03069618  0.01155332 0.5209513
    ## [1] "Oxytocin ~ genotype in FND only"
    ##               Df Sum Sq Mean Sq F value Pr(>F)
    ## rs53576        2   52.3  26.145   1.762  0.183
    ## psychMed       1    7.1   7.114   0.480  0.492
    ## bdi            1    2.1   2.090   0.141  0.709
    ## stai1          1    2.2   2.168   0.146  0.704
    ## date_diff      1    8.4   8.411   0.567  0.455
    ## contraception  2   14.9   7.435   0.501  0.609
    ## menopause      1   21.1  21.076   1.421  0.239
    ## age            1    0.5   0.514   0.035  0.853
    ## Residuals     48  712.1  14.836
    ##   Tukey multiple comparisons of means
    ##     95% family-wise confidence level
    ## 
    ## Fit: aov(formula = lm(formula = Oxytocin ~ rs53576 + psychMed + bdi + stai1 + date_diff + contraception + menopause + age, data = dfOT_FND))
    ## 
    ## $rs53576
    ##            diff       lwr       upr     p adj
    ## GA-AA -0.336900 -3.608400 2.9345999 0.9664077
    ## GG-AA -2.157227 -5.500287 1.1858327 0.2724510
    ## GG-GA -1.820327 -4.543494 0.9028396 0.2485237
    ## [1] "OXTR Methylation ~ genotype in HC only"
    ##               Df  Sum Sq  Mean Sq F value Pr(>F)  
    ## rs53576        2 0.00219 0.001096   0.837 0.4386  
    ## bdi            1 0.00006 0.000057   0.043 0.8359  
    ## stai1          1 0.00010 0.000100   0.076 0.7833  
    ## date_diff      1 0.00018 0.000177   0.136 0.7142  
    ## contraception  2 0.00365 0.001824   1.394 0.2571  
    ## menopause      1 0.00489 0.004885   3.732 0.0587 .
    ## age            1 0.00184 0.001843   1.408 0.2407  
    ## Residuals     53 0.06938 0.001309                 
    ## ---
    ## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    ## 2 observations deleted due to missingness
    ##   Tukey multiple comparisons of means
    ##     95% family-wise confidence level
    ## 
    ## Fit: aov(formula = lm(formula = OXTR_CpG_sum ~ rs53576 + bdi + stai1 + date_diff + contraception + menopause + age, data = dfOT_HC))
    ## 
    ## $rs53576
    ##                diff         lwr        upr     p adj
    ## GA-AA -0.0009642857 -0.03951329 0.03758472 0.9979963
    ## GG-AA -0.0131818182 -0.05336302 0.02699938 0.7101315
    ## GG-GA -0.0122175325 -0.03595437 0.01151931 0.4346302
    ## [1] "Oxytocin ~ genotype in HC only"
    ##               Df Sum Sq Mean Sq F value Pr(>F)
    ## rs53576        2    4.6   2.295   0.166  0.848
    ## bdi            1    0.0   0.011   0.001  0.977
    ## stai1          1    4.2   4.228   0.305  0.583
    ## date_diff      1   11.2  11.159   0.805  0.373
    ## contraception  2   28.7  14.363   1.036  0.362
    ## menopause      1    3.5   3.462   0.250  0.619
    ## age            1   17.4  17.369   1.253  0.268
    ## Residuals     55  762.3  13.860
    ##   Tukey multiple comparisons of means
    ##     95% family-wise confidence level
    ## 
    ## Fit: aov(formula = lm(formula = Oxytocin ~ rs53576 + bdi + stai1 + date_diff + contraception + menopause + age, data = dfOT_HC))
    ## 
    ## $rs53576
    ##             diff       lwr      upr     p adj
    ## GA-AA  0.1773611 -3.776918 4.131640 0.9935862
    ## GG-AA -0.3937899 -4.504615 3.717035 0.9710913
    ## GG-GA -0.5711510 -2.964910 1.822608 0.8341175

    4 The effect of Childhood Trauma

    As a next step we investigate how childhood trauma might interact with OT, OXTR Methlyation and genotype. I acknowledge that the term “traumatized” versus “no-traumatized” is maybe not optimal, but it refers to the CTQ total score cut off of > 35.

    ## [1] "Number of traumatized versus non-traumatized:"
    ##                  
    ##                   FND HC
    ##   non-traumatized  24 40
    ##   traumatized      35 25
    ## [1] "Association OT with CTQ stratification, OXTR Methylation and Genotype in FND:"
    ##                                Df Sum Sq Mean Sq F value Pr(>F)  
    ## group_ctq                       1   27.4   27.38   1.998 0.1656  
    ## OXTR_CpG_sum                    1   84.2   84.22   6.146 0.0177 *
    ## rs53576                         2   53.7   26.87   1.961 0.1548  
    ## psychMed                        1    0.4    0.39   0.029 0.8664  
    ## bdi                             1    8.3    8.28   0.604 0.4417  
    ## stai1                           1    0.6    0.58   0.042 0.8385  
    ## date_diff                       1   11.3   11.30   0.825 0.3695  
    ## contraception                   2   14.3    7.17   0.523 0.5969  
    ## menopause                       1    1.3    1.34   0.098 0.7562  
    ## age                             1    7.3    7.29   0.532 0.4701  
    ## group_ctq:OXTR_CpG_sum          1    1.4    1.40   0.102 0.7514  
    ## group_ctq:rs53576               2    0.6    0.31   0.022 0.9778  
    ## OXTR_CpG_sum:rs53576            2   37.4   18.68   1.363 0.2681  
    ## group_ctq:OXTR_CpG_sum:rs53576  2   10.7    5.36   0.391 0.6788  
    ## Residuals                      38  520.7   13.70                 
    ## ---
    ## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    ## 1 observation deleted due to missingness
    ## [1] "Association OT with CTQ stratification, OXTR Methylation and Genotype in HC:"
    ##                                Df Sum Sq Mean Sq F value Pr(>F)  
    ## group_ctq                       1    3.9    3.91   0.289 0.5937  
    ## OXTR_CpG_sum                    1   38.3   38.33   2.828 0.0996 .
    ## rs53576                         2    7.2    3.60   0.266 0.7679  
    ## bdi                             1    1.6    1.60   0.118 0.7326  
    ## stai1                           1    2.5    2.50   0.184 0.6696  
    ## date_diff                       1    9.1    9.07   0.669 0.4176  
    ## contraception                   2   53.4   26.72   1.971 0.1511  
    ## menopause                       1    0.1    0.06   0.004 0.9469  
    ## age                             1   33.6   33.63   2.481 0.1222  
    ## group_ctq:OXTR_CpG_sum          1    9.1    9.09   0.670 0.4172  
    ## group_ctq:rs53576               2   11.9    5.97   0.441 0.6465  
    ## OXTR_CpG_sum:rs53576            2   10.0    4.99   0.368 0.6943  
    ## group_ctq:OXTR_CpG_sum:rs53576  1   39.3   39.30   2.899 0.0955 .
    ## Residuals                      45  609.9   13.55                 
    ## ---
    ## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    ## 2 observations deleted due to missingness

    Results: We see that not in FND nor in HC there is an association between CTQ stratification group. So no differences in OT, OXTR Methylation or genotype depending on whether you have been CTQ total yes or no. We can look into the individual subscales, just to be sure.

    Let’s plot it, independent of genotype. We plot correlations with each CTQ subtype and OXTR Methylation (left) as well as Oxytocin (right). However, there are no significant correlations between OXTR Methylation/Oxytocin and the different childhood traumata.

    4.1 CTQ Emotional Neglect

    There is no significant association with childhood emotional neglect, nor an interaction effect.

    ## [1] "Oxytocin ~ Emotional Neglect *group + with genotype and methlyation as covariate"
    ##                   Df Sum Sq Mean Sq F value Pr(>F)
    ## ctq_emoneg         1   10.9  10.858   0.783  0.378
    ## group              1    7.8   7.768   0.560  0.456
    ## rs53576            2   56.7  28.339   2.043  0.135
    ## OXTR_CpG_sum       1    0.1   0.129   0.009  0.923
    ## psychMed           1    7.8   7.796   0.562  0.455
    ## bdi                1    0.7   0.711   0.051  0.821
    ## stai1              1    1.2   1.186   0.085  0.771
    ## date_diff          1   15.6  15.572   1.122  0.292
    ## contraception      2   37.0  18.518   1.335  0.268
    ## menopause          1    1.2   1.175   0.085  0.772
    ## age                1    6.9   6.864   0.495  0.483
    ## ctq_emoneg:group   1    5.3   5.279   0.380  0.539
    ## Residuals        106 1470.6  13.873               
    ## 3 observations deleted due to missingness
    ## [1] "Oxytocin ~ Emotional Neglect *group + with genotype and methlyation as covariate"
    ##                   Df  Sum Sq  Mean Sq F value   Pr(>F)    
    ## ctq_emoneg         1 0.00007 0.000073   0.062 0.804186    
    ## group              1 0.00286 0.002857   2.429 0.122068    
    ## rs53576            2 0.02246 0.011232   9.552 0.000153 ***
    ## Oxytocin           1 0.00001 0.000013   0.011 0.917190    
    ## psychMed           1 0.00293 0.002929   2.490 0.117525    
    ## bdi                1 0.00257 0.002571   2.186 0.142212    
    ## stai1              1 0.00027 0.000267   0.227 0.634745    
    ## date_diff          1 0.00004 0.000041   0.035 0.851911    
    ## contraception      2 0.01206 0.006028   5.126 0.007497 ** 
    ## menopause          1 0.00905 0.009054   7.699 0.006531 ** 
    ## age                1 0.00081 0.000813   0.691 0.407716    
    ## ctq_emoneg:group   1 0.00073 0.000725   0.617 0.434052    
    ## Residuals        106 0.12465 0.001176                     
    ## ---
    ## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    ## 3 observations deleted due to missingness

    4.2 CTQ Emotional Abuse

    There is no interesting significant association with childhood emotional Abuse, nor an interaction effect.
    There is a negative correlation in healthy controls between Oxytocin levels and emotional abuse in GG carriers.

    ## [1] "Oxytocin ~ Emotional Abuse *group + with genotype and methlyation as covariate"
    ##                  Df Sum Sq Mean Sq F value Pr(>F)
    ## ctq_emoab         1    1.9   1.879   0.134  0.715
    ## group             1   10.7  10.667   0.762  0.385
    ## rs53576           2   55.1  27.534   1.966  0.145
    ## OXTR_CpG_sum      1    0.2   0.175   0.012  0.911
    ## psychMed          1    9.7   9.742   0.695  0.406
    ## bdi               1    2.3   2.259   0.161  0.689
    ## stai1             1    1.2   1.232   0.088  0.767
    ## date_diff         1   14.4  14.403   1.028  0.313
    ## contraception     2   32.7  16.331   1.166  0.316
    ## menopause         1    2.9   2.899   0.207  0.650
    ## age               1    5.6   5.584   0.399  0.529
    ## ctq_emoab:group   1    0.3   0.349   0.025  0.875
    ## Residuals       106 1484.7  14.007               
    ## 3 observations deleted due to missingness
    ## [1] "Oxytocin ~ Emotional Abuse *group + with genotype and methlyation as covariate"
    ##                  Df  Sum Sq  Mean Sq F value   Pr(>F)    
    ## ctq_emoab         1 0.00196 0.001964   1.716 0.193016    
    ## group             1 0.00360 0.003603   3.149 0.078854 .  
    ## rs53576           2 0.02088 0.010440   9.124 0.000221 ***
    ## Oxytocin          1 0.00002 0.000017   0.015 0.902886    
    ## psychMed          1 0.00344 0.003439   3.005 0.085897 .  
    ## bdi               1 0.00141 0.001413   1.234 0.269050    
    ## stai1             1 0.00018 0.000180   0.157 0.692449    
    ## date_diff         1 0.00008 0.000079   0.069 0.793773    
    ## contraception     2 0.01215 0.006073   5.307 0.006359 ** 
    ## menopause         1 0.00913 0.009134   7.982 0.005646 ** 
    ## age               1 0.00082 0.000822   0.719 0.398461    
    ## ctq_emoab:group   1 0.00355 0.003546   3.099 0.081220 .  
    ## Residuals       106 0.12129 0.001144                     
    ## ---
    ## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    ## 3 observations deleted due to missingness

    4.3 CTQ Physical Neglect

    There is no significant association with childhood Physical Neglect, nor an interaction effect.

    ## [1] "Oxytocin ~ Physical Neglect *group + with genotype and methlyation as covariate"
    ##                    Df Sum Sq Mean Sq F value Pr(>F)
    ## ctq_physneg         1    0.5   0.528   0.038  0.846
    ## group               1   11.6  11.639   0.832  0.364
    ## rs53576             2   54.0  26.996   1.930  0.150
    ## OXTR_CpG_sum        1    0.1   0.137   0.010  0.921
    ## psychMed            1   10.7  10.698   0.765  0.384
    ## bdi                 1    3.3   3.282   0.235  0.629
    ## stai1               1    1.3   1.254   0.090  0.765
    ## date_diff           1   13.9  13.932   0.996  0.321
    ## contraception       2   32.7  16.337   1.168  0.315
    ## menopause           1    2.9   2.925   0.209  0.648
    ## age                 1    5.9   5.916   0.423  0.517
    ## ctq_physneg:group   1    1.8   1.803   0.129  0.720
    ## Residuals         106 1482.9  13.989               
    ## 3 observations deleted due to missingness
    ## [1] "Oxytocin ~ Physical Neglect *group + with genotype and methlyation as covariate"
    ##                    Df  Sum Sq  Mean Sq F value   Pr(>F)    
    ## ctq_physneg         1 0.00310 0.003105   2.700 0.103287    
    ## group               1 0.00324 0.003244   2.822 0.095934 .  
    ## rs53576             2 0.02115 0.010577   9.200 0.000207 ***
    ## Oxytocin            1 0.00001 0.000013   0.012 0.914485    
    ## psychMed            1 0.00357 0.003574   3.109 0.080756 .  
    ## bdi                 1 0.00118 0.001176   1.023 0.314135    
    ## stai1               1 0.00022 0.000217   0.189 0.664872    
    ## date_diff           1 0.00011 0.000110   0.096 0.757188    
    ## contraception       2 0.01147 0.005735   4.988 0.008503 ** 
    ## menopause           1 0.00866 0.008656   7.529 0.007131 ** 
    ## age                 1 0.00070 0.000696   0.605 0.438304    
    ## ctq_physneg:group   1 0.00323 0.003233   2.812 0.096494 .  
    ## Residuals         106 0.12187 0.001150                     
    ## ---
    ## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    ## 3 observations deleted due to missingness

    4.4 CTQ Physical Abuse

    There is no significant association with childhood Physical Abuse, nor an interaction effect.

    ## [1] "Oxytocin ~ Physical Abuse *group + with genotype and methlyation as covariate"
    ##                   Df Sum Sq Mean Sq F value Pr(>F)
    ## ctq_physab         1    5.2   5.227   0.375  0.542
    ## group              1    9.2   9.176   0.658  0.419
    ## rs53576            2   55.2  27.624   1.980  0.143
    ## OXTR_CpG_sum       1    0.2   0.229   0.016  0.898
    ## psychMed           1   10.1  10.100   0.724  0.397
    ## bdi                1    1.3   1.287   0.092  0.762
    ## stai1              1    1.0   1.005   0.072  0.789
    ## date_diff          1   15.0  15.035   1.078  0.302
    ## contraception      2   34.3  17.129   1.228  0.297
    ## menopause          1    3.5   3.504   0.251  0.617
    ## age                1    5.2   5.246   0.376  0.541
    ## ctq_physab:group   1    2.8   2.760   0.198  0.657
    ## Residuals        106 1478.6  13.949               
    ## 3 observations deleted due to missingness
    ## [1] "Oxytocin ~ Physical Abuse *group + with genotype and methlyation as covariate"
    ##                   Df  Sum Sq  Mean Sq F value  Pr(>F)    
    ## ctq_physab         1 0.00066 0.000657   0.557 0.45696    
    ## group              1 0.00330 0.003301   2.800 0.09721 .  
    ## rs53576            2 0.02259 0.011294   9.581 0.00015 ***
    ## Oxytocin           1 0.00002 0.000022   0.019 0.89051    
    ## psychMed           1 0.00293 0.002933   2.488 0.11769    
    ## bdi                1 0.00181 0.001805   1.532 0.21862    
    ## stai1              1 0.00022 0.000220   0.187 0.66654    
    ## date_diff          1 0.00006 0.000063   0.053 0.81798    
    ## contraception      2 0.01174 0.005869   4.979 0.00858 ** 
    ## menopause          1 0.00886 0.008860   7.516 0.00718 ** 
    ## age                1 0.00087 0.000874   0.741 0.39113    
    ## ctq_physab:group   1 0.00050 0.000505   0.428 0.51433    
    ## Residuals        106 0.12495 0.001179                    
    ## ---
    ## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    ## 3 observations deleted due to missingness

    4.5 CTQ Sexual Abuse

    There is no significant association with childhood Sexual Neglect, nor an interaction effect.

    ## [1] "Oxytocin ~ Sexual Abuse *group + with genotype and methlyation as covariate"
    ##                  Df Sum Sq Mean Sq F value Pr(>F)
    ## ctq_sexab         1    5.4   5.449   0.390  0.534
    ## group             1   11.1  11.149   0.798  0.374
    ## rs53576           2   54.4  27.223   1.948  0.148
    ## OXTR_CpG_sum      1    0.1   0.069   0.005  0.944
    ## psychMed          1   10.7  10.741   0.769  0.383
    ## bdi               1    0.8   0.833   0.060  0.808
    ## stai1             1    1.1   1.094   0.078  0.780
    ## date_diff         1   15.2  15.198   1.088  0.299
    ## contraception     2   31.6  15.820   1.132  0.326
    ## menopause         1    2.8   2.826   0.202  0.654
    ## age               1    7.0   7.033   0.503  0.480
    ## ctq_sexab:group   1    0.0   0.043   0.003  0.956
    ## Residuals       106 1481.1  13.973               
    ## 3 observations deleted due to missingness
    ## [1] "Oxytocin ~ Sexual Abuse *group + with genotype and methlyation as covariate"
    ##                  Df  Sum Sq  Mean Sq F value  Pr(>F)    
    ## ctq_sexab         1 0.00006 0.000055   0.047 0.82857    
    ## group             1 0.00247 0.002468   2.102 0.15003    
    ## rs53576           2 0.02288 0.011438   9.745 0.00013 ***
    ## Oxytocin          1 0.00001 0.000007   0.006 0.93934    
    ## psychMed          1 0.00269 0.002690   2.292 0.13303    
    ## bdi               1 0.00357 0.003575   3.046 0.08386 .  
    ## stai1             1 0.00029 0.000288   0.245 0.62133    
    ## date_diff         1 0.00002 0.000023   0.019 0.88991    
    ## contraception     2 0.01177 0.005883   5.012 0.00832 ** 
    ## menopause         1 0.00886 0.008864   7.552 0.00705 ** 
    ## age               1 0.00110 0.001100   0.937 0.33513    
    ## ctq_sexab:group   1 0.00039 0.000390   0.332 0.56546    
    ## Residuals       106 0.12442 0.001174                    
    ## ---
    ## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    ## 3 observations deleted due to missingness

    5 Oxytocin, OXTR Methylation, Genotype and their relation to Brain Volume.

    For the genetics project, we have already prepared the brain volumes that are significantly associated with the genotype in FND. Here the table as a summary:


    Let’s have a look at Oxytocin and the brain volume of some specific ROIs. I just make here really rough models where I include a Oxytocin x OXTR Methylation x Genotype tripple interaciton. We can disentangle this together in the next meeting if needed, but I think it would be nore interessting to focus on fMRI.

    ## [1] "Amygdala Brain Volume in FND: "

    ## [1] "Amygdala Brain Volume in HC: "
    ##                               Df Sum Sq Mean Sq F value   Pr(>F)    
    ## Oxytocin                       1 0.0142  0.0142   0.436  0.51269    
    ## OXTR_CpG_sum                   1 0.2623  0.2623   8.068  0.00705 ** 
    ## rs53576                        2 0.0180  0.0090   0.277  0.75932    
    ## TIV                            1 0.6894  0.6894  21.204 4.13e-05 ***
    ## gender                         1 0.7222  0.7222  22.213 2.94e-05 ***
    ## bdi                            1 0.0222  0.0222   0.682  0.41378    
    ## stai2                          1 0.0511  0.0511   1.573  0.21710    
    ## date_diff                      1 0.0127  0.0127   0.392  0.53473    
    ## contraception                  2 0.0693  0.0347   1.066  0.35384    
    ## age                            1 0.3252  0.3252  10.003  0.00298 ** 
    ## ctq                            1 0.0021  0.0021   0.065  0.80075    
    ## Oxytocin:OXTR_CpG_sum          1 0.0175  0.0175   0.537  0.46781    
    ## Oxytocin:rs53576               2 0.0472  0.0236   0.725  0.49052    
    ## OXTR_CpG_sum:rs53576           2 0.0029  0.0014   0.044  0.95686    
    ## Oxytocin:OXTR_CpG_sum:rs53576  2 0.0831  0.0416   1.279  0.28953    
    ## Residuals                     40 1.3005  0.0325                     
    ## ---
    ## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    ## 2 observations deleted due to missingness
    ## [1] "Hippocampus Brain Volume in FND: "
    ##                               Df Sum Sq Mean Sq F value Pr(>F)  
    ## Oxytocin                       1  1.136  1.1357   2.291 0.1388  
    ## OXTR_CpG_sum                   1  0.007  0.0069   0.014 0.9067  
    ## rs53576                        2  0.387  0.1933   0.390 0.6799  
    ## TIV                            1  2.989  2.9893   6.031 0.0190 *
    ## gender                         1  0.636  0.6360   1.283 0.2648  
    ## bdi                            1  0.000  0.0002   0.000 0.9833  
    ## stai2                          1  0.028  0.0276   0.056 0.8149  
    ## date_diff                      1  1.544  1.5443   3.116 0.0860 .
    ## contraception                  2  0.564  0.2819   0.569 0.5712  
    ## age                            1  0.831  0.8312   1.677 0.2036  
    ## ctq                            1  0.065  0.0651   0.131 0.7192  
    ## Oxytocin:OXTR_CpG_sum          1  2.638  2.6376   5.322 0.0269 *
    ## Oxytocin:rs53576               2  0.208  0.1039   0.210 0.8119  
    ## OXTR_CpG_sum:rs53576           2  0.016  0.0082   0.017 0.9836  
    ## Oxytocin:OXTR_CpG_sum:rs53576  2  0.221  0.1107   0.223 0.8009  
    ## Residuals                     36 17.842  0.4956                 
    ## ---
    ## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    ## 1 observation deleted due to missingness
    ## [1] "Hippocampus Brain Volume in HC: "
    ##                               Df Sum Sq Mean Sq F value  Pr(>F)   
    ## Oxytocin                       1  0.355   0.355   1.000 0.32334   
    ## OXTR_CpG_sum                   1  1.411   1.411   3.969 0.05320 . 
    ## rs53576                        2  1.575   0.787   2.215 0.12235   
    ## TIV                            1  4.200   4.200  11.817 0.00138 **
    ## gender                         1  1.966   1.966   5.531 0.02369 * 
    ## bdi                            1  0.069   0.069   0.194 0.66172   
    ## stai2                          1  0.609   0.609   1.715 0.19786   
    ## date_diff                      1  0.783   0.783   2.203 0.14555   
    ## contraception                  2  0.457   0.229   0.643 0.53083   
    ## age                            1  2.527   2.527   7.111 0.01100 * 
    ## ctq                            1  0.103   0.103   0.290 0.59316   
    ## Oxytocin:OXTR_CpG_sum          1  0.555   0.555   1.562 0.21862   
    ## Oxytocin:rs53576               2  2.889   1.445   4.065 0.02472 * 
    ## OXTR_CpG_sum:rs53576           2  0.456   0.228   0.641 0.53208   
    ## Oxytocin:OXTR_CpG_sum:rs53576  2  0.168   0.084   0.236 0.79056   
    ## Residuals                     40 14.216   0.355                   
    ## ---
    ## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    ## 2 observations deleted due to missingness
    ## [1] "Insula Brain Volume in FND: "
    ##                               Df Sum Sq Mean Sq F value   Pr(>F)    
    ## Oxytocin                       1   0.00    0.00   0.001 0.969625    
    ## OXTR_CpG_sum                   1   3.57    3.57   1.408 0.243184    
    ## rs53576                        2   0.82    0.41   0.161 0.851685    
    ## TIV                            1  42.30   42.30  16.683 0.000235 ***
    ## gender                         1   0.04    0.04   0.016 0.900559    
    ## bdi                            1   2.27    2.27   0.894 0.350734    
    ## stai2                          1   1.87    1.87   0.736 0.396662    
    ## date_diff                      1   4.58    4.58   1.807 0.187228    
    ## contraception                  2   9.81    4.91   1.936 0.159055    
    ## age                            1  12.63   12.63   4.982 0.031932 *  
    ## ctq                            1   1.34    1.34   0.528 0.472257    
    ## Oxytocin:OXTR_CpG_sum          1   2.03    2.03   0.800 0.376996    
    ## Oxytocin:rs53576               2   0.56    0.28   0.110 0.896160    
    ## OXTR_CpG_sum:rs53576           2   4.74    2.37   0.935 0.401874    
    ## Oxytocin:OXTR_CpG_sum:rs53576  2   5.66    2.83   1.117 0.338389    
    ## Residuals                     36  91.27    2.54                     
    ## ---
    ## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    ## 1 observation deleted due to missingness
    ## [1] "Insula Brain Volume in HC: "
    ##                               Df Sum Sq Mean Sq F value   Pr(>F)    
    ## Oxytocin                       1   5.75   5.749   4.101 0.049567 *  
    ## OXTR_CpG_sum                   1   3.02   3.018   2.153 0.150133    
    ## rs53576                        2   3.12   1.561   1.113 0.338440    
    ## TIV                            1  13.01  13.014   9.283 0.004083 ** 
    ## gender                         1  18.05  18.046  12.873 0.000899 ***
    ## bdi                            1   2.36   2.362   1.685 0.201750    
    ## stai2                          1   0.24   0.236   0.168 0.683751    
    ## date_diff                      1  10.98  10.978   7.831 0.007863 ** 
    ## contraception                  2   6.00   3.000   2.140 0.130904    
    ## age                            1  16.33  16.335  11.652 0.001481 ** 
    ## ctq                            1   0.23   0.233   0.166 0.685719    
    ## Oxytocin:OXTR_CpG_sum          1   0.24   0.235   0.168 0.684154    
    ## Oxytocin:rs53576               2   4.32   2.158   1.540 0.226919    
    ## OXTR_CpG_sum:rs53576           2   1.44   0.721   0.514 0.601792    
    ## Oxytocin:OXTR_CpG_sum:rs53576  2   2.41   1.207   0.861 0.430323    
    ## Residuals                     40  56.07   1.402                     
    ## ---
    ## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    ## 2 observations deleted due to missingness

    6 Oxytocin and functional connectivity in FND

    What have I done?
    I calculated functional connectivity according to standard procedures. This gives me - using the AAL atlas 4005 individual brain connections. I then calculate their correlation with Oxytocin connectionwise. This gives me a 90x90 correlation matrix at which the “blobs” show those connections that significantly correlate with OT.

    Correlations in HC (left) and FND (right) with a significance level of alpha = 0.01. I am a bit more strict, as we have many connections, FDR however does not need to be applied as these are correlations and not a statistical test.


    We now further investigate the significant correlations in FND only.
    The following connections significantly correlate with Oxytocin in FND:

  • Frontal_Inf_Oper_L - Frontal_Inf_Oper_R with Correlation Coefficient =-0.38256 with p =0.0043048
  • Frontal_Inf_Tri_R - Frontal_Inf_Oper_R with Correlation Coefficient =0.34786 with p =0.0099516
  • Olfactory_R - Frontal_Inf_Oper_R with Correlation Coefficient =-0.34842 with p =0.0098263
  • Frontal_Mid_Orb_Medial_L - Frontal_Inf_Oper_R with Correlation Coefficient =-0.35015 with p =0.009442
  • Frontal_Mid_Orb_Medial_L - Frontal_Inf_Oper_R with Correlation Coefficient =-0.40187 with p =0.0025944
  • Cingulum_Post_L - Frontal_Inf_Oper_R with Correlation Coefficient =-0.36745 with p =0.0062697
  • Frontal_Mid_Orb_Medial_L - Frontal_Inf_Oper_R with Correlation Coefficient =-0.42675 with p =0.001291
  • Hippocampus_L - Frontal_Inf_Oper_R with Correlation Coefficient =-0.40558 with p =0.0023457
  • Frontal_Inf_Oper_L - Frontal_Inf_Oper_R with Correlation Coefficient =-0.3679 with p =0.0062008
  • Frontal_Inf_Oper_L - Frontal_Inf_Oper_R with Correlation Coefficient =-0.3754 with p =0.0051551
  • Frontal_Inf_Tri_L - Frontal_Inf_Oper_R with Correlation Coefficient =-0.38471 with p =0.0040744
  • Frontal_Inf_Oper_L - Frontal_Inf_Oper_R with Correlation Coefficient =-0.45634 with p =0.00052391
  • Frontal_Inf_Tri_L - Frontal_Inf_Oper_R with Correlation Coefficient =0.3493 with p =0.0096298
  • Occipital_Mid_L - Frontal_Inf_Oper_R with Correlation Coefficient =0.36517 with p =0.0066256
  • Hippocampus_L - Frontal_Inf_Oper_R with Correlation Coefficient =-0.35623 with p =0.0081965
  • ParaHippocampal_L - Frontal_Inf_Oper_R with Correlation Coefficient =-0.36281 with p =0.007012
  • Frontal_Inf_Oper_L - Frontal_Inf_Oper_R with Correlation Coefficient =0.37525 with p =0.0051746
  • Parietal_Sup_L - Frontal_Inf_Oper_R with Correlation Coefficient =-0.35122 with p =0.0092127
  • Caudate_R - Frontal_Inf_Oper_R with Correlation Coefficient =-0.39996 with p =0.0027313
  • Precuneus_L - Frontal_Inf_Oper_R with Correlation Coefficient =-0.41394 with p =0.0018615
  • Olfactory_L - Frontal_Inf_Oper_R with Correlation Coefficient =-0.34835 with p =0.0098423
  • Session info

    ## R version 4.2.1 (2022-06-23)
    ## Platform: x86_64-apple-darwin17.0 (64-bit)
    ## Running under: macOS Big Sur ... 10.16
    ## 
    ## Matrix products: default
    ## BLAS:   /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRblas.0.dylib
    ## LAPACK: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRlapack.dylib
    ## 
    ## locale:
    ## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
    ## 
    ## attached base packages:
    ## [1] stats     graphics  grDevices utils     datasets  methods   base     
    ## 
    ## other attached packages:
    ##  [1] viridis_0.6.4     viridisLite_0.4.1 ggtext_0.1.2      mediation_4.5.0  
    ##  [5] sandwich_3.0-2    corrplot_0.92     SNPassoc_2.0-17   forcats_0.5.2    
    ##  [9] stringr_1.5.0     purrr_1.0.1       readr_2.1.3       tidyr_1.2.1      
    ## [13] tibble_3.2.1      tidyverse_1.3.2   dplyr_1.1.2       multcomp_1.4-20  
    ## [17] TH.data_1.1-1     survival_3.4-0    mvtnorm_1.1-3     plyr_1.8.8       
    ## [21] lme4_1.1-30       Matrix_1.5-1      rstatix_0.7.1     ggpubr_0.5.0     
    ## [25] lawstat_3.5       car_3.1-2         carData_3.0-5     readxl_1.4.1     
    ## [29] plotly_4.10.1     Hmisc_5.0-1       ggplot2_3.4.2     MASS_7.3-58.1    
    ## [33] ISwR_2.0-8       
    ## 
    ## loaded via a namespace (and not attached):
    ##   [1] utf8_1.2.2                             
    ##   [2] rms_6.3-0                              
    ##   [3] tidyselect_1.2.0                       
    ##   [4] RSQLite_2.2.18                         
    ##   [5] AnnotationDbi_1.58.0                   
    ##   [6] htmlwidgets_1.6.4                      
    ##   [7] grid_4.2.1                             
    ##   [8] BiocParallel_1.30.4                    
    ##   [9] lpSolve_5.6.18                         
    ##  [10] munsell_0.5.0                          
    ##  [11] codetools_0.2-18                       
    ##  [12] withr_2.5.0                            
    ##  [13] colorspace_2.1-0                       
    ##  [14] Biobase_2.56.0                         
    ##  [15] filelock_1.0.2                         
    ##  [16] highr_0.9                              
    ##  [17] knitr_1.40                             
    ##  [18] rstudioapi_0.14                        
    ##  [19] stats4_4.2.1                           
    ##  [20] ggsignif_0.6.4                         
    ##  [21] labeling_0.4.2                         
    ##  [22] MatrixGenerics_1.8.1                   
    ##  [23] Rdpack_2.4                             
    ##  [24] GenomeInfoDbData_1.2.8                 
    ##  [25] farver_2.1.1                           
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    ##  [28] generics_0.1.3                         
    ##  [29] xfun_0.37                              
    ##  [30] timechange_0.1.1                       
    ##  [31] BiocFileCache_2.4.0                    
    ##  [32] markdown_1.5                           
    ##  [33] R6_2.5.1                               
    ##  [34] GenomeInfoDb_1.32.4                    
    ##  [35] Kendall_2.2.1                          
    ##  [36] bitops_1.0-7                           
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    ##  [38] DelayedArray_0.22.0                    
    ##  [39] assertthat_0.2.1                       
    ##  [40] BiocIO_1.6.0                           
    ##  [41] scales_1.2.1                           
    ##  [42] nnet_7.3-18                            
    ##  [43] googlesheets4_1.0.1                    
    ##  [44] gtable_0.3.1                           
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    ##  [46] MatrixModels_0.5-1                     
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    ##  [57] GenomicFeatures_1.48.4                 
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    ##  [60] tools_4.2.1                            
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    ##  [84] matrixStats_0.62.0                     
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    ##  [96] mgcv_1.8-41                            
    ##  [97] tzdb_0.3.0                             
    ##  [98] Formula_1.2-4                          
    ##  [99] lubridate_1.9.0                        
    ## [100] DBI_1.1.3                              
    ## [101] dbplyr_2.2.1                           
    ## [102] rappdirs_0.3.3                         
    ## [103] boot_1.3-28                            
    ## [104] BiocStyle_2.24.0                       
    ## [105] cli_3.4.1                              
    ## [106] rbibutils_2.2.13                       
    ## [107] parallel_4.2.1                         
    ## [108] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2
    ## [109] GenomicRanges_1.48.0                   
    ## [110] pkgconfig_2.0.3                        
    ## [111] GenomicAlignments_1.32.1               
    ## [112] haplo.stats_1.8.9                      
    ## [113] foreign_0.8-83                         
    ## [114] xml2_1.3.3                             
    ## [115] bslib_0.4.0                            
    ## [116] XVector_0.36.0                         
    ## [117] rvest_1.0.3                            
    ## [118] VariantAnnotation_1.42.1               
    ## [119] digest_0.6.30                          
    ## [120] Biostrings_2.64.1                      
    ## [121] rmarkdown_2.20                         
    ## [122] cellranger_1.1.0                       
    ## [123] htmlTable_2.4.1                        
    ## [124] restfulr_0.0.15                        
    ## [125] curl_4.3.3                             
    ## [126] commonmark_1.8.1                       
    ## [127] Rsamtools_2.12.0                       
    ## [128] quantreg_5.94                          
    ## [129] rjson_0.2.21                           
    ## [130] nloptr_2.0.3                           
    ## [131] lifecycle_1.0.3                        
    ## [132] nlme_3.1-160                           
    ## [133] jsonlite_1.8.3                         
    ## [134] BSgenome_1.64.0                        
    ## [135] fansi_1.0.3                            
    ## [136] pillar_1.9.0                           
    ## [137] lattice_0.20-45                        
    ## [138] KEGGREST_1.36.3                        
    ## [139] fastmap_1.1.0                          
    ## [140] httr_1.4.4                             
    ## [141] glue_1.6.2                             
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    ## [143] bit_4.0.4                              
    ## [144] stringi_1.7.8                          
    ## [145] sass_0.4.2                             
    ## [146] blob_1.2.3                             
    ## [147] polspline_1.1.20                       
    ## [148] org.Hs.eg.db_3.15.0                    
    ## [149] memoise_2.0.1